Article Per Year (5 Year)
p-Index From 2021 - 2026
8.412
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering ComEngApp : Computer Engineering and Applications Journal Jurnal Teliska TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Sinergi Annual Research Seminar EMITTER International Journal of Engineering Technology Proceeding of the Electrical Engineering Computer Science and Informatics VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika Journal of Applied Smart Electrical Network and System (JASENS) Jurnal Techno Bahari Jurnal Rekayasa Elektro Sriwijaya International Journal of Research in Vocational Studies (IJRVOCAS) Aptekmas : Jurnal Pengabdian Kepada Masyarakat International Journal of Advanced Science Computing and Engineering SmartComp Jurnal Polimesin International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) TECHNOLOGIC Jurnal Elektronika dan Telekomunikasi
Tresna Dewi
Politeknik Negeri Sriwijaya
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 69 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 69 Documents
Search

 3   4   5   6   7 
MODEL PREDIKSI ENERGI HARVESTING PADA KARPET PINTAR PIEZOELEKTRIK BERBASIS MULTILAYER PERCEPTRON (MLP) DENGAN VARIASI POLA LANGKAH MANUSIA Muhammad Billy Akbar; Pola Risma; Tresna Dewi; Hendra Marta Yuda
Technologic Vol 17 No 1 (2026): TECHNOLOGIC
Publisher : LPPM Politeknik Astra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52453/technologic.v17i1.517

Abstract

Penelitian ini mengembangkan model prediksi berbasis Multi-Layer Perceptron (MLP) untuk output energi piezoelectric smart carpet, dengan fokus pada lima variasi pola langkah manusia: lari, jalan cepat, normal, lambat, dan hentak. Berbeda dari penelitian sebelumnya yang menggunakan pola langkah seragam, penelitian ini memodelkan keberagaman gait sebagai variabel laju gerak (Rate of Motion) dinamis untuk mencerminkan perilaku pejalan kaki yang lebih realistis. Data eksperimental dikumpulkan dari 5 subjek dengan variasi berat 54–98 kg menggunakan konfigurasi rangkaian seri-paralel piezoelectric. Model MLP dirancang dengan arsitektur 3 hidden layer (64, 32, 16 neuron), learning rate 0.001, dan mekanisme early stopping, dengan input berat subjek dan laju gerak serta output tegangan, arus, dan daya. Hasil evaluasi menunjukkan R² sebesar 0.8654 (tegangan), 0.8759 (arus), dan 0.8873 (daya), dengan MAPE masing-masing 9.55%, 10.42%, dan 30.22%. Nilai MAPE daya yang lebih tinggi merupakan konsekuensi matematis dari propagasi error pada P = V × I. Analisis korelasi mengkonfirmasi laju gerak sebagai faktor dominan (r = 0.706–0.721) dibanding berat (r = 0.303–0.491). Model ini diarahkan untuk implementasi di pintu masuk Gedung Jurusan Teknik Elektro Politeknik Negeri Sriwijaya.
BENCHMARKING ARIMAX DAN SUPPORT VECTOR REGRESSION (SVR) UNTUK PREDIKSI BIOMASSA IKAN NILA DALAM SISTEM AKUAKULTUR PINTAR Agung Tantowi Junior; Tresna Dewi; Pola Risma
Technologic Vol 17 No 1 (2026): TECHNOLOGIC
Publisher : LPPM Politeknik Astra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52453/technologic.v17i1.519

Abstract

Estimasi biomassa Ikan Nila secara presisi merupakan elemen krusial dalam sistem Smart Aquaculture untuk mengoptimalkan Feed Conversion Ratio dan mencegah overfeeding. Tantangan utama pemodelan ini adalah fluktuasi data deret waktu yang non-linear akibat dinamika kualitas air, serta keterbatasan jumlah sampel observasi lapangan (dataset kecil). Penelitian ini membandingkan kinerja algoritma statistik AutoRegressive Integrated Moving Average (ARIMAX) dan Support Vector Regression (SVR) dengan kernel Radial Basis Function (RBF) untuk memprediksi bobot ikan harian. Dataset dikumpulkan selama 11 minggu dari kolam intensif, meliputi variabel bobot, pakan kumulatif, dan Oksigen Terlarut (Dissolved Oxygen, DO). Validasi model menerapkan skema Walk-Forward Validation. Hasil pengujian menunjukkan SVR secara signifikan mengungguli ARIMAX, menghasilkan Mean Absolute Percentage Error (MAPE) sebesar 6.08% dan R² Score 0.9191, dibandingkan ARIMAX dengan MAPE 11.97% dan R² Score 0.7569. SVR terbukti lebih responsif menangkap anomali perlambatan laju pertumbuhan ikan yang dipicu fase stres hipoksia. Kesimpulannya, model SVR memiliki komputasi inferensi efisien sehingga prospektif ditanamkan pada mikrokontroler IoT berbasis ESP32 guna mengendalikan aktuator pemberian pakan otomatis secara dinamis.
ANALISIS KOMPARATIF LINEAR REGRESSION DAN QUADRATIC REGRESSION DALAM OPTIMASI DEBIT AIR TERHADAP PERTUMBUHAN SELADA PADA SISTEM HIDROPONIK NFT BERBASIS IOT Ragil Alfarizi; Muhammad Nawawi; Tresna Dewi
Technologic Vol 17 No 1 (2026): TECHNOLOGIC
Publisher : LPPM Politeknik Astra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52453/technologic.v17i1.521

Abstract

Penelitian ini bertujuan untuk mengkaji hubungan antara debit air dan laju pertumbuhan tanaman selada pada sistem hidroponik Nutrient Film Technique (NFT), sekaligus menentukan kisaran debit air yang optimal menggunakan pendekatan model linear regression dan quadratic regression. Sistem pemantauan debit air dikembangkan berbasis Internet of Things (IoT) dengan memanfaatkan mikrokontroler ESP32-S3, flow sensor AICHI OF05ZAT, serta platform Blynk untuk memperoleh data secara real-time. Pengambilan data dilakukan selama dua minggu pada lima gully, kemudian dianalisis menggunakan kedua model regresi untuk mengidentifikasi karakteristik hubungan linier dan non-linier antara debit air dan pertumbuhan tanaman. Hasil analisis menunjukkan bahwa model linear regression menghasilkan nilai koefisien determinasi (R2) sebesar 0,067 yang mengindikasikan hubungan linier yang lemah antara debit air dan laju pertumbuhan tanaman. Sebaliknya, model quadratic regression menghasilkan nilai R2 sebesar 0,651 yang menunjukkan adanya hubungan non-linier dengan pola parabola terbuka ke bawah. Temuan ini menunjukkan bahwa model quadratic regression lebih mampu merepresentasikan hubungan antara debit air dan pertumbuhan tanaman dibandingkan model linier. Berdasarkan model tersebut, laju pertumbuhan maksimum tanaman selada diperoleh pada kisaran debit air sekitar ±2,9 L/min.
Implementasi Deep Learning Dalam Prediksi Real-Time Iradian Surya Angga Liwijaya; Pola Risma; Yurni Oktarina; Tresna Dewi
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/pvdpsr36

Abstract

Accurate prediction of solar irradiance plays a critical role in the planning and operation of renewable energy systems, particularly for photovoltaic integration and energy management. This study investigates the use of a deep learning approach based solely on Convolutional Neural Networks (CNN) to forecast short-term solar irradiance values. The model is trained using normalized multivariate time series data, which include several meteorological parameters as input features. The CNN architecture is designed to extract temporal patterns from the input sequences and predict radiation intensity at the next time step. Experimental results show that the proposed model achieves strong predictive performance, with a Mean Squared Error (MSE) of 0.0006, Root Mean Squared Error (RMSE) of 0.0242, Mean Absolute Error (MAE) of 0.0184, and a coefficient of determination (R²) of 0.9607. These findings demonstrate that CNN, despite its simplicity, is capable of effectively learning complex temporal relationships in solar irradiance data. Furthermore, the loss curves for both training and validation sets indicate stable convergence without signs of overfitting. The results suggest that CNN-based forecasting models can offer a lightweight and accurate solution for real-time solar prediction applications, especially when computational resources are limited.
Model Deep Learning Hybrid CNN-AE untuk Klasifikasi Presisi Warna Buah Melon Yurni Oktarina; Tresna Dewi; Dini Septiyani AR
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/d3hydf28

Abstract

Melon fruit color classification is a critical step in assessing fruit ripeness and quality. This study proposes a hybrid deep learning model that integrates Convolutional Neural Network (CNN) and Attention Enhancement (AE) for accurate classification of melon fruit color. The model leverages CNN’s strength in visual feature extraction while enhancing focus on crucial image regions through the attention mechanism. A diverse image dataset of melon fruits was collected under various lighting conditions and angles. Pre-processing steps, including data augmentation, normalization, and image scaling, were applied to improve model generalization. The CNN-Attention hybrid architecture incorporates an attention module into the CNN layers to emphasize significant features. Comparative experiments between the standard CNN and the hybrid model demonstrate that the latter achieves superior classification accuracy, with an average improvement of 5%. Moreover, the hybrid model exhibits better robustness against image noise and lighting variations. These results indicate that incorporating Attention Enhancement can yield a more adaptive and reliable model for melon fruit color classification. The proposed approach is expected to support the development of automated systems for fruit sorting in agriculture and distribution, enhancing speed, accuracy, and efficiency for farmers, traders, and consumers.
Exploring YOLO-Based Deep Learning Approaches for Fish Detection in Intelligent Aquatic Monitoring Systems Tresna Dewi; Riyo Irawan; Agum Try Wardhana; Muhammad Amri Yahya; Lukman Nul Hakim; Dini Septiyani AR
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.1007

Abstract

The Advancements in precision aquaculture demand robust visual monitoring systems capable of accurate, real-time fish detection in complex underwater environments characterized by turbidity, occlusion, and dynamic illumination. While YOLO (You Only Look Once) architectures have demonstrated high efficiency in object detection tasks, their comparative performance for underwater fish detection remains underexplored, particularly across recent variants such as YOLOv5, YOLOv8, and YOLOv11. This study presents a systematic evaluation of three state-of-the-art YOLO models using a curated GlowFish dataset consisting of 533 annotated images across three fluorescent species. Data were acquired under controlled but visually diverse conditions using multi-angle imaging and standardized illumination. A uniform training pipeline, consistent annotation using the COCO format, and identical hyperparameters were applied across models to ensure fair benchmarking. Key evaluation metrics include precision, recall, mAP@0.5, and mAP@0.5:0.95. Experimental results reveal that YOLOv5 achieved the highest precision (0.963) and mAP@0.5 (0.967), while YOLOv8 delivered superior recall (0.930) and more balanced detection across species classes. YOLOv11 demonstrated architectural potential but showed greater sensitivity to class imbalance and reduced confidence stability. Visual analysis and confusion matrices further confirmed model-specific trade-offs in classification reliability and localization precision. This work contributes critical empirical insights into the selection of YOLO architectures for intelligent aquaculture systems, offering practical guidance for real-time aquatic monitoring deployments. Future research will extend this framework to multi-species, multi-environment datasets, integrate spatiotemporal behavioral tracking, and investigate deployment on resource-constrained edge-AI platforms, advancing the field toward interpretable and autonomous aquatic monitoring solutions.
Sequential integration of optimized LSTM-RNN base-learners into a stacking ensemble for industrial PV output prediction Nur Mutiara Syahrian; Tresna Dewi; Rusdianasari Rusdianasari
Jurnal Polimesin Vol 24, No 4 (2026): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i4.9478

Abstract

Accurate photovoltaic (PV) power forecasting is critical for managing solar intermittency, especially within industrial power systems. The objective of this study is to develop a high-precision forecasting framework specifically tuned for the stochastic weather patterns at the Pertamina RU III Sungai Gerong facility. This study proposes a two-stage deep learning framework utilizing a full year of historical operational data (June 2024-June 2025), totaling 2,795 samples, to ensure it captures a wide range of seasonal variations. The system optimized RNN and LSTM models as base learners which were then integrated by an MLP meta-learner through a Stacking Ensemble. The results demonstrate that a Tapered MLP (64-32) configuration performs best, minimizing the Root Mean Squared Error (RMSE) to 130.26 kWh and maximizing the R² index to 0.816. The Storm Simulation scenario was constructed using an interactive environment to replicate stochastic solar radiation drops exceeding 50% of peak capacity caused by sudden cloud movement. The results reveal that the ensemble model maintains a low error rate of 5.3%, outperforming standalone models which failed with errors up to 19.7%. These findings has a potential to support proactive load-shifting and synchronization in industrial-scale PV systems.
Hybrid PV–VAWT Automatic Fish Feeding System for Sustainable Aquaculture Aulia, M Habib; Dewi, Tresna; Bow, Yohandri
Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika Vol. 10 No. 2 (2026): July
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/e-saintika.v10i2.5769

Abstract

This study developed and experimentally evaluated an automatic fish feeding system powered by a hybrid photovoltaic (PV)–Vertical Axis Wind Turbine (VAWT) energy architecture for small-scale aquaculture applications. The prototype integrated PV generation, a VAWT, battery storage, charge control, a DC–AC inverter, a programmable timer, and an auger-based feeding mechanism. An engineering Design and Development approach was applied through prototype design, assembly, laboratory testing, and field evaluation. VAWT performance was evaluated under controlled laboratory conditions and at two field environments, Lubuk Bakung and Tanjung Api-Api Port, while PV performance was assessed with and without the feeder load. The two automatic feeder units required a combined electrical power of 52 W. The PV subsystem produced a maximum of 76.85 W without feeder load and 60.80 W under feeder load, providing an 8.80-W (16.9%) instantaneous margin above feeder demand at peak output. VAWT maximum power reached 17.304 W in the laboratory, 17.93 W at Tanjung Api-Api, and 0.689 W at Lubuk Bakung, demonstrating substantial site-dependent variation in wind-energy contribution. Battery input voltage remained within 12.4–12.6 V during the Lubuk Bakung observations, while the inverter supplied 208–218 V AC under the tested light-load condition. The feeding mechanism completed the observed programmed cycles without mechanical interruption or pellet blockage. Overall, the findings demonstrate the technical feasibility of a PV-dominant, VAWT-supplemented, battery-buffered architecture for autonomous automatic fish feeding under the tested conditions.
Modeling and Forecasting Piezoelectric Energy Harvesting Using Deep LSTM–ANN Architectures Yurni Oktarina; Tresna Dewi; Muhammad Amri Yahya; Assyifa Mourlina Faraquinnsha; Denny Juraijin
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.1002

Abstract

Piezoelectric energy harvesting (PEH) enables maintenance-free micro-power generation for autonomous sensing and ultra-low-power electronics by converting ambient mechanical excitation into electrical energy. Despite substantial progress in piezoelectric materials and device structures, forecasting PEH electrical outputs remains difficult because the response is nonlinear, excitation is stochastic, and performance can drift under repeated loading. This paper proposes a hybrid deep learning architecture that integrates Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN) components to forecast voltage, current, and power from footstep-driven PEH time-series data. The dataset is constructed by sampling the harvester voltage under controlled walking-induced excitation and organizing the continuous signal into supervised samples using a sliding-window scheme; features are normalized and paired with future targets for multi-output regression. The model is trained and evaluated against standalone LSTM, standalone ANN, and classical forecasting baselines using RMSE, MAE, MSE, and R2. Experimental results show high voltage prediction accuracy (R2=0.9896, RMSE = 0.0035, MAE = 0.0022), while current and power are predicted with acceptable performance consistent with their higher noise sensitivity and nonlinear coupling. These findings indicate that combining temporal memory with nonlinear regression improves forecasting stability for PEH outputs within the defined experimental setting and provides a practical basis for energy-aware scheduling and monitoring in self-powered sensing applications. Future work will extend the dataset to broader excitation conditions and incorporate uncertainty-aware modeling for robust edge deployment.
Co-Authors A Rahman Agum Try Wardhana Agung Tantowi Junior Ahmad Fudholi Alkausar, Muhammad Fajri Amalia, Kania Yusriani Amperawan Amperawan Amperawan Amperawan, Amperawan Angga Liwijaya Angga Prasetia Anggraini, Citra Arissetyadhi, Iwan Assyifa Mourlina Faraquinnsha Aulia, M Habib Auliya, Annisa Azhar, M. Sayid Badruzzaman, Farid Bambang Tutuko Bambang, Muhammad Refo Billi Clinton Bimo, Muhammad Clinton, Billi Dadi Setiadi Daniesar, Muhammad Nouval Denny Juraijin Dicky Astra Yudha Didi Suhaedi Dinata, Yogi Dini Septiyani AR Edo Triyandi Erwin Harahap Evelina Ginting Fajar, Yusuf Fatahul Arifin, Fatahul Fradina Septiarini Hendra Marta Yuda Hendra Marta Yudha Hibrizi, Dzaky Rafif Husni, Nyayu Latifah INDRAYANI INDRAYANI Indriyani Indriyani Junaedi, Ketut Juwita, Aulia Ratna Kemala Dewi Kusumanto, Raden Lukman Nul Hakim M. Muhajir Mardianto, Yudhi Mardiyati, Elsa Nurul Maulidina, Elfira Mayastri Devana Muhammad Amri Yahya Muhammad Billy Akbar Muhammad Dede Yusuf Muhammad Insan Kamil, Muhammad Insan Muhammad Nawawi Muhammad Ridho Kenawas Muhammad Roriz Muhammad Taufik Roseno Mulya, Zarqa Muslikhin Mustofa Mustofa Neta Larasati Noer, Mohammad Nawawi Nur Mutiara Syahrian Oktarina, Yurni Oktarina, Yurni Pola Risma Putri Repina Kesuma Ragil Alfarizi Rapli Wijaya RD Kusumanto RD Kusumanto Rendi Dwi Yanto Renny Maulidda, Renny Rinaldi Rinaldi Riyo Irawan Robiansyah Ronald Sukwadi Roseno, M. Taufik Rusdianasari Rusdianasari Rusdianasari Rusdianasari Rusdianasari Sakuraba, Takahiro Sasmanto, Andri Agus Sastiani, Destri Zumar SELAMET MUSLIMIN Siproni Siproni Siproni Umar Siti Afiyah Qatrunnada Siti Nurmaini Sri Rezki Artini Syahrian, Nur Mutiara Tampubolon, Debora Utami, Retyo Wizi Nafa Velia Yuliza Wahju, Marsellinus Bachtiar Wijanarko, Yudi Wijaya Pratama, Agung Yohandri Bow Yudha Wira Pratama Yudi Wijanarko Yudi Wijanarko, Yudi Yurika Islamiati Yurni Oktarina Yurni Oktarina Yurni Oktarina Yusi, Muhammad Syahirman Zarqa Mulya